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Michael J Tildesley; Sadie J Ryan
Publisher: Public Library of Science (PLoS)
Journal: PLoS Computational Biology
Languages: English
Types: Article
Subjects: Computational Biology, Applied Mathematics, Social and Behavioral Sciences, Computer Modeling, Research Article, Biology, Mathematics, Veterinary Science, Veterinary Diseases, QH301-705.5, Theoretical Biology, SF, Computer Science, Mathematical Computing, Geography, Veterinary Epidemiology, Biology (General)
The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.

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  • NSF | NCEAS: National Center for ...
  • NSF | National Institute for Math...

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